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Hoehn KB, Kleinstein SH. B cell phylogenetics in the single cell era. Trends Immunol 2024; 45:62-74. [PMID: 38151443 PMCID: PMC10872299 DOI: 10.1016/j.it.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
The widespread availability of single-cell RNA sequencing (scRNA-seq) has led to the development of new methods for understanding immune responses. Single-cell transcriptome data can now be paired with B cell receptor (BCR) sequences. However, RNA from BCRs cannot be analyzed like most other genes because BCRs are genetically diverse within individuals. In humans, BCRs are shaped through recombination followed by mutation and selection for antigen binding. As these processes co-occur with cell division, B cells can be studied using phylogenetic trees representing the mutations within a clone. B cell trees can link experimental timepoints, tissues, or cellular subtypes. Here, we review the current state and potential of how B cell phylogenetics can be combined with single-cell data to understand immune responses.
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Affiliation(s)
- Kenneth B Hoehn
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
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2
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Marquez S, Babrak L, Greiff V, Hoehn KB, Lees WD, Luning Prak ET, Miho E, Rosenfeld AM, Schramm CA, Stervbo U. Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis. Methods Mol Biol 2022; 2453:297-316. [PMID: 35622333 PMCID: PMC9761518 DOI: 10.1007/978-1-0716-2115-8_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Ulrik Stervbo
- Center for Translational Medicine, Immunology, and Transplantation, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany. .,Immundiagnostik, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.
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Hoehn KB, Turner JS, Miller FI, Jiang R, Pybus OG, Ellebedy AH, Kleinstein SH. Human B cell lineages associated with germinal centers following influenza vaccination are measurably evolving. eLife 2021; 10:e70873. [PMID: 34787567 PMCID: PMC8741214 DOI: 10.7554/elife.70873] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/11/2021] [Indexed: 11/23/2022] Open
Abstract
The poor efficacy of seasonal influenza virus vaccines is often attributed to pre-existing immunity interfering with the persistence and maturation of vaccine-induced B cell responses. We previously showed that a subset of vaccine-induced B cell lineages are recruited into germinal centers (GCs) following vaccination, suggesting that affinity maturation of these lineages against vaccine antigens can occur. However, it remains to be determined whether seasonal influenza vaccination stimulates additional evolution of vaccine-specific lineages, and previous work has found no significant increase in somatic hypermutation among influenza-binding lineages sampled from the blood following seasonal vaccination in humans. Here, we investigate this issue using a phylogenetic test of measurable immunoglobulin sequence evolution. We first validate this test through simulations and survey measurable evolution across multiple conditions. We find significant heterogeneity in measurable B cell evolution across conditions, with enrichment in primary response conditions such as HIV infection and early childhood development. We then show that measurable evolution following influenza vaccination is highly compartmentalized: while lineages in the blood are rarely measurably evolving following influenza vaccination, lineages containing GC B cells are frequently measurably evolving. Many of these lineages appear to derive from memory B cells. We conclude from these findings that seasonal influenza virus vaccination can stimulate additional evolution of responding B cell lineages, and imply that the poor efficacy of seasonal influenza vaccination is not due to a complete inhibition of vaccine-specific B cell evolution.
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Affiliation(s)
- Kenneth B Hoehn
- Department of Pathology, Yale School of MedicineNew HavenUnited States
| | - Jackson S Turner
- Department of Pathology and Immunology, Washington University School of MedicineSt LouisUnited States
| | | | - Ruoyi Jiang
- Department of Immunobiology, Yale School of MedicineNew HavenUnited States
| | - Oliver G Pybus
- Department of Zoology, University of OxfordOxfordUnited Kingdom
| | - Ali H Ellebedy
- Department of Pathology and Immunology, Washington University School of MedicineSt LouisUnited States
- The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of MedicineSt LouisUnited States
| | - Steven H Kleinstein
- Department of Pathology, Yale School of MedicineNew HavenUnited States
- Department of Immunobiology, Yale School of MedicineNew HavenUnited States
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale UniversityNew HavenUnited States
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Natali EN, Babrak LM, Miho E. Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics. Front Immunol 2021; 12:574411. [PMID: 34211454 PMCID: PMC8239437 DOI: 10.3389/fimmu.2021.574411] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 05/17/2021] [Indexed: 01/02/2023] Open
Abstract
Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1-5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research.
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Affiliation(s)
- Eriberto N. Natali
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Lmar M. Babrak
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- aiNET GmbH, Basel, Switzerland
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Nourmohammad A, Otwinowski J, Łuksza M, Mora T, Walczak AM. Fierce Selection and Interference in B-Cell Repertoire Response to Chronic HIV-1. Mol Biol Evol 2020; 36:2184-2194. [PMID: 31209469 PMCID: PMC6759071 DOI: 10.1093/molbev/msz143] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
During chronic infection, HIV-1 engages in a rapid coevolutionary arms race with the host's adaptive immune system. While it is clear that HIV exerts strong selection on the adaptive immune system, the characteristics of the somatic evolution that shape the immune response are still unknown. Traditional population genetics methods fail to distinguish chronic immune response from healthy repertoire evolution. Here, we infer the evolutionary modes of B-cell repertoires and identify complex dynamics with a constant production of better B-cell receptor (BCR) mutants that compete, maintaining large clonal diversity and potentially slowing down adaptation. A substantial fraction of mutations that rise to high frequencies in pathogen-engaging CDRs of BCRs are beneficial, in contrast to many such changes in structurally relevant frameworks that are deleterious and circulate by hitchhiking. We identify a pattern where BCRs in patients who experience larger viral expansions undergo stronger selection with a rapid turnover of beneficial mutations due to clonal interference in their CDR3 regions. Using population genetics modeling, we show that the extinction of these beneficial mutations can be attributed to the rise of competing beneficial alleles and clonal interference. The picture is of a dynamic repertoire, where better clones may be outcompeted by new mutants before they fix.
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Affiliation(s)
- Armita Nourmohammad
- Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany.,Department of Physics, University of Washington, Seattle, WA
| | - Jakub Otwinowski
- Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Thierry Mora
- Laboratoire de Physique Statistique, CNRS, Sorbonne University, Paris-Diderot University, École Normale Supérieure (PSL), Paris, France
| | - Aleksandra M Walczak
- Laboratoire de Physique Théorique, CNRS, Sorbonne University, École Normale Supérieure (PSL), Paris, France
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Hoehn KB, Vander Heiden JA, Zhou JQ, Lunter G, Pybus OG, Kleinstein SH. Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination. Proc Natl Acad Sci U S A 2019; 116:22664-22672. [PMID: 31636219 PMCID: PMC6842591 DOI: 10.1073/pnas.1906020116] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
In order to produce effective antibodies, B cells undergo rapid somatic hypermutation (SHM) and selection for binding affinity to antigen via a process called affinity maturation. The similarities between this process and evolution by natural selection have led many groups to use phylogenetic methods to characterize the development of immunological memory, vaccination, and other processes that depend on affinity maturation. However, these applications are limited by the fact that most phylogenetic models are designed to be applied to individual lineages comprising genetically diverse sequences, while B cell repertoires often consist of hundreds to thousands of separate low-diversity lineages. Further, several features of affinity maturation violate important assumptions in standard phylogenetic models. Here, we introduce a hierarchical phylogenetic framework that integrates information from all lineages in a repertoire to more precisely estimate model parameters while simultaneously incorporating the unique features of SHM. We demonstrate the power of this repertoire-wide approach by characterizing previously undescribed phenomena in affinity maturation. First, we find evidence consistent with age-related changes in SHM hot-spot targeting. Second, we identify a consistent relationship between increased tree length and signs of increased negative selection, apparent in the repertoires of recently vaccinated subjects and those without any known recent infections or vaccinations. This suggests that B cell lineages shift toward negative selection over time as a general feature of affinity maturation. Our study provides a framework for undertaking repertoire-wide phylogenetic testing of SHM hypotheses and provides a means of characterizing dynamics of mutation and selection during affinity maturation.
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Affiliation(s)
- Kenneth B Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520
| | - Jason A Vander Heiden
- Department of Bioinformatics & Computational Biology, Genentech, South San Francisco, CA 94080
| | - Julian Q Zhou
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511
| | - Gerton Lunter
- Wellcome Centre for Human Genetics, Oxford OX3 7BN, United Kingdom
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520;
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511
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Abstract
Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given datasets. This procedure is well developed in the Bayesian perspective, in which one infers probability distributions describing to what extent various possible parameters agree with the data. In this paper, we motivate and review probabilistic modeling for adaptive immune receptor repertoire data then describe progress and prospects for future work, from germline haplotyping to adaptive immune system deployment across tissues. The relevant quantities in immune sequence analysis include not only continuous parameters such as gene use frequency but also discrete objects such as B-cell clusters and lineages. Throughout this review, we unravel the many opportunities for probabilistic modeling in adaptive immune receptor analysis, including settings for which the Bayesian approach holds substantial promise (especially if one is optimistic about new computational methods). From our perspective, the greatest prospects for progress in probabilistic modeling for repertoires concern ancestral sequence estimation for B-cell receptor lineages, including uncertainty from germline genotype, rearrangement, and lineage development.
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Affiliation(s)
- Branden Olson
- Computational Biology Program Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Mail stop: M1-B514 Seattle, WA 98109-1024 phone: +1 206 667 7318
| | - Frederick A. Matsen
- Computational Biology Program Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Mail stop: M1-B514 Seattle, WA 98109-1024 phone: +1 206 667 7318
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